11 LLM Model Comparison: GPT, BERT, XLNET & Claude

Feature	GPT	BERT	XLNet	Claude
Architecture	Transformer decoder-only	Transformer encoder-only	Transformer XL / permutation-based	Transformer-based LLM, likely decoder-focused with alignment safety layers
Pretraining Objective	Causal LM (predict next token left-to-right)	Masked LM (predict masked tokens) + Next Sentence Prediction	Permutation LM (predict tokens in all orders)	Causal LM + reinforcement learning from human feedback (RLHF)
Context	Unidirectional (left-to-right)	Bidirectional	Bidirectional via permutation	Likely unidirectional, extended context via memory/attention optimizations
Generation	Excellent for text generation	Not suited for generation (fine-tuning needed)	Can generate but more complex than GPT	Strong generative abilities, designed for safe, helpful responses
Fine-tuning Tasks	Text generation, summarization, dialogue	Classification, QA, NER	Classification, QA, some generative tasks	Dialogue, summarization, instruction-following, question answering
Strengths	Natural generative capabilities	Strong understanding of context & semantics	Combines bidirectional understanding with autoregressive modeling	Human-aligned, safe outputs, instruction-following
Weaknesses	Limited bidirectional context	Cannot generate text naturally	More complex training & generation	Possibly slower or more constrained than GPT in freeform generation